Argentina vs Philippines

Argentina vs Philippines: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 331.5 nights in Philippines, in the CMIP6 climate models’ median run. In 2014 the value was higher in Philippines, by 288.9 nights, or 7.77 times that in Argentina.

nights a year · CMIP6

Argentina vs Philippines: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 331.5 nights in Philippines, in the CMIP6 climate models’ median run. In 2014 the value was higher in Philippines, by 288.9 nights, or 7.77 times that in Argentina.
ArgentinaPhilippinesCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Philippines has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Argentina at 65.2 nights and Philippines at 351.3 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaPhilippines
201442.6331.5
201341.8330.9
201241331.1
201141.5329.8
201040.8330.6
200941.6329
200841.4327.5
200739.6327.8
200639.3328.7
200540.1329.7
200440.6329.4
200338.7327.1
200240.2328.9
200139.5327.8
200039.5326.8
199938.4327.5
199838.8326.5
199738.5325.4
199637324.6
199535.7324.2
199434.7321.5
199334.3319.5
199233.4318.4
199136.8324.1
199036323.5
198937322.5
198836.9324
198736.1323.1
198636.4322.1
198536321.4
198434.7321.7
198335.2319.4
198235.7320.5
198137.3320.4
198036.3322.7
197937.5321.2
197835.7320.6
197735.1319.8
197635.7319.6
197534.8319.6
197435.4320
197334.1319.4
197235.5318.9
197134.7318.8
197034.9319
196933.4318.7
196834.6317.2
196733317.4
196634.3318.8
196532.2317.2
196430.3314.9
196332317.2
196235.2318.6
196135.6319.3
196035.6318.6
195934.5320.5
195833.3321.8
195735.7319.4
195635320
195534.4319.3
195433.6317.7
195335.8320
195234.4319
195135.4319.8
195033.9320.4
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaPhilippines
201544.433.5–53.4333319.8–339.5
201643.532.4–53.4334.2322.8–340.7
201742.834.2–52.6333.4319.7–339.8
201842.632.7–52.6333.2320.1–340.1
201943.633.4–53.1334.1323.4–341.2
202045.735.5–57.2334.6322.6–341.8
202145.634.3–56.4335.6323.3–341.1
202244.633.3–56.4335.7323.3–342.1
202344.934.9–53.9336.7324.8–343.2
202445.834.2–57.5337.2325.7–343.9
202546.536–58.1336.3326.2–342.8
202647.936.7–57336.5322.9–343
202746.434.5–56.3336.2325.7–342.3
202846.133.5–58.5337.1325.5–343.8
202948.336–58.8338.1327.2–344.6
203047.835.4–60.3337.7326.2–344.4
203146.935.5–58.7337.1325.6–343.8
20324837.9–59.7339.4329.5–346.1
203347.436.7–57.6339.7329.6–345.5
203447.837.8–58.9338.8328.8–345
203547.339–59338.6327.5–346.4
203648.939.2–58.4339.6327.8–346.9
20375141.2–60.9339.8329.1–345.5
203848.737.9–59.7341.9330.7–347.7
203948.839.1–58.4341.2330.4–346.9
204050.440.4–62.3340.4328.1–347.6
204150.740.4–61.7341.1329.1–346.7
20425037.3–61.1342331.4–347.7
204350.340.5–61341.3329–348.1
20445240–65342.8329.9–349.8
204552.441.3–65.6343.4333.8–348.7
204651.941.2–62.7342.7330.2–348.7
204751.542–64343.2331.7–350.6
204854.542.6–65343.3332.7–350.6
204953.942.8–64.9343.2332.5–349.5
205052.543.6–62.4344.3332.6–349.9
205155.743.3–67.4344.4334.9–350.8
205255.642.2–67344.1333.5–350.9
205355.342.6–67.6344.3333.6–350.3
205454.743–66.7343.4332–349.9
205555.942.3–67.2344.3334.4–350.5
205657.642.8–68.6345334.6–352.6
205756.842.3–68.7345.5335.1–351.5
205854.841.5–68.1345.8334.3–351.3
205957.844.9–70.3345.5333.7–351.6
206056.943.4–67.9346.8335.5–353.5
206156.542.3–70.6345.9335–351.8
206257.345.8–68.5346335.2–353.5
206358.244.6–70.2346.6336.2–352.8
206458.845.8–71.5346.9335.3–353
206558.746–71.1346.1335.9–351.9
206659.947–68.9346.5335.4–352.4
206758.944.8–71.5346.1335.4–352
206857.844.3–70.3347.1335.1–354.1
206959.445.4–70.5347333.8–353.6
207061.348.2–74.3346.7336.1–352.7
207160.148.7–73.2347.6336.2–353.9
207260.146.7–73.7347334.4–353.8
207360.847.6–73.5348.4336.7–355
207460.447.6–73.7348.6337–354.9
207561.347.9–73.9347.9337.2–354
207661.146.8–75.8348.4337–355.1
207760.647.1–73.1348.7337–354.8
207863.449.4–75.6348.4338.2–354.7
207959.948.7–73348.8337.6–354.7
20806348.1–75.5349.1338.4–355.9
208161.448.7–73.5348.8336.9–355.1
208261.747.7–75349.6338–354.9
208362.148.2–75.6349.9337.9–356.1
208462.950.3–76349.5336.7–356.4
20856246.9–76.4349.1338.2–355.4
208662.749.4–76.4349.4337.5–354.9
208761.847.9–74.9349.7338.7–356
208864.450.8–77.5349.9338.9–356.1
208962.749.3–76.3350.2339.8–355.2
209064.152.5–77.1350.1339.7–355.9
209164.451.7–78.3349.5339.5–356.3
209265.650.8–78.3350.5339.3–356.1
209362.749.7–76.8350.4337.9–356.5
209463.950.1–78.5350.5338.4–355.9
209562.948.8–77.9350.5338.3–355.7
209664.749.4–78.9351.1339–358
209764.552.4–80.1350.2339.3–355.6
209865.652.7–78.7350.3337.4–355.2
209965.249.3–80.2350.6339.3–356.4
210065.250–81.7351.3340.2–356.3

Each country on its own

Good to know

  • Nights in a year when the temperature stays above 20 °C, averaged over the country.
  • Source: World Bank Climate Change Knowledge Portal, CMIP6 multi-model median.
  • The dashed line is the CMIP6 model median (SSP2-4.5, middle of the road), a scenario rather than a forecast; the band is its models’ 10th–90th percentile.
  • Climate model values are the median of the CMIP6 models, run on past emissions to 2014 and on SSP2-4.5, a middle-of-the-road path, after: they are not measurements.

How the layers work, where their data comes from and what they can’t tell you: Countries through time

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